PID Plots for Conference

Run 2

Deuteron ID

Deuterons are taken from a 2016 sample, and other tracks are from a 2016 sample, for all momenta. Decay products in the sample are removed, such that the sample is a close representation of a MinBias sample.

Yields of each track type is normalised to unity.

Profile histogram of ProbNNd in bins of momentum for MC tracks in range 0 < p < 100 GeV.c. MC samples are the same as above. Features in the shapes can be attributed to the RICH thresholds, where separation between the particles changes.

2017

RICH

Kaon identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Proton identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different \DeltaLLPPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Proton identification efficiency and kaon misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different \DeltaLLPK requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

MUON

Muon identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuPi), respectively.

Muon identification efficiency and kaon misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuK), respectively.

Muon identification efficiency and proton misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuP), respectively.

2016

RICH

Kaon identification efficiency and pion misidentification rate as measured using 2016 MagDown data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Kaon identification efficiency and pion misidentification rate as measured using 2016 MagUp data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Kaon identification efficiency and pion misidentification rate as measured using 2016 data (MagDown + MagUp) as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Kaon identification efficiency and pion misidentification rate as measured using 2016 data with different \DeltaLLKPi requirements.

MUON

Muon identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuPi), respectively.

Muon identification efficiency and kaon misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuK), respectively.

Muon identification efficiency and proton misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuP), respectively.

2015

RICH

Kaon identification efficiency and pion misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Proton identification efficiency and kaon misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLPK requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Proton identification efficiency and pion misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLPPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

MUON

Efficiency of the isMuon selection based on the matching of hits in the muon system to track extrapolation as a function of momentum (black) and efficiency of the isMuon selection plus \DeltaLL(\mu-h) >0, where h can be a pion (red), a kaon (blue) and a proton (green).

Misidentification probability of pions as a function of momentum after isMuon (black) and isMuon+\DeltaLL(\mu - \pi) >0 (red).

Misidentification probability of kaons as a function of momentum after isMuon (black) and isMuon+\DeltaLL(\mu - K) >0 (blue).

Misidentification probability of protons as a function of momentum after isMuon (black) and isMuon+\DeltaLL(\mu - P) >0 (green).

CALO

Electron identification efficiency and pion misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLePi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. The plot is made only for electron track for which no bremsstrahlung photons have been recovered.

Run 1

2012

RICH

Kaon identification efficiency and pion misidentification rate as measured using 2012 data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Proton identification efficiency and kaon misidentification rate as measured using 2012 data as a function of track momentum. Two different \DeltaLLPK requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Proton identification efficiency and pion misidentification rate as measured using 2012 data as a function of track momentum. Two different \DeltaLLPPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

2011

RICH

Reconstructed Cherenkov angle for \emph{isolated} tracks, as a function of track momentum in the \cfourften radiator. The Cherenkov bands for muons, pions, kaons and protons are clearly visible.

Kaon identification efficiency and pion misidentification rate as measured using data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Kaon identification efficiency and pion misidentification rate from simulation as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

Pion misidentification fraction versus kaon identification efficiency as measured in 7\,TeV LHCb collisions as a function of track multiplicity. The efficiencies are averaged over all particle momenta.

Pion misidentification fraction versus kaon identification efficiency as measured in 7\,TeV LHCb collisions as a function of the number of reconstructed primary vertices. The efficiencies are averaged over all particle momenta.

CALO

Comparison of the Monte Carlo performance of the gamma/pi0 separation tool developed by Yandex (full lines) and the default one (dashed line). The curves display the photon selection efficiency versus the pi0 selection efficiency as determined on a photon sample (B -> K*gamma) and on two pi0 samples (B -> Kpipi0 in blue and B -> J/psi K*[Kpi0] in green). The red dots indicate 95% photon efficency and the corresponding efficiency for pi0. The figure is taken from the proceedings of the ACAT 2017 conference.

Performance of the photon identification. Purity as a function of efficiency for (green) the full photon candidate sample, (blue) converted candidates according to the SPD information and (red) non-converted candidates (left). Photon identification efficiency as a function of \piz rejection efficiency for the $\gamma-\piz$ separation tool for simulation, the red curve, and data, the blue curve (right).

Global

Electron identification performance using the $\deltaLLCombepi$ variable, as measured in 8\,TeV collision data, using a tag and probe technique with electrons from the decay $B^{\pm} \to (J/\psi \to e^+e^-) K^{\pm}$. Pion misidentication rate versus electron identification probability when the cut value is varied.

Electron identification performance using the $\deltaLLCombepi$ variable, as measured in 8\,TeV collision data, using a tag and probe technique with electrons from the decay $B^{\pm} \to (J/\psi \to e^+e^-) K^{\pm}$. Electron identification efficiency and pion misidentification rate as a function of track momentum, for two different cuts on $\deltaLLCombepi$.

Background misidentification rates versus muon identification efficiency, as measured in the $\Sigma^+\to p\mu^+\mu^-$ decay study. The variables $\deltaLLXpi$ (black) and ProbNN (red), the probability value for each particle hypothesis, are compared for $5-10$\gevc muons and $5-50$\gevc protons, using data sidebands for backgrounds and Monte Carlo simulation for the signal.

Background misidentification rates versus proton identification efficiency, as measured in the $\Sigma^+\to p\mu^+\mu^-$ decay study. The variables $\deltaLLXpi$ (black) and ProbNN (red), the probability value for each particle hypothesis, are compared for $5-10$\gevc muons and $5-50$\gevc protons, using data sidebands for backgrounds and Monte Carlo simulation for the signal.